Predictive Analytics In 2017 – Will It Pick Up?

By Rushal Patel

Predictive analytic can help you create experiences that resonate with clients and leads. This will help you to create an effective and streamlined marketing plan that gets your startup’s message across.

Have you ever marveled at the fact that Netflix seems to know exactly what to recommend in your queue whenever you load your screen and settle in for a night of eating popcorn in front of a good show or movie? You can thank predictive analytic for those smart recommendations. The reality is that you don’t have to be a media giant like Netflix to take advantage of this amazing technology. In fact, startup B2B technology companies are actually the players that have the most to gain as the mechanisms behind predictive analytic becomes easier to use and adopt into pre-existing marketing and sales plans.

What We Know About Marketing Analytics

Most B2B marketers already have a pretty good understanding of the importance of marketing analytics. A survey by Regalix earlier this year revealed the following things:

  • 82 percent of B2B companies use marketing analytics tools
  • Of the 18 percent remaining, 67 percent said they would be using marketing analytics tools in next 12 months

While use of analytical tools is widespread, many B2B companies are actually behind when it comes to keeping up with the newest resources that are available to enhance analytical capabilities. What’s missing? Not every company is up to speed when it comes integrating analytical technologies into their websites and customer portals.

Defining Predictive Analytics

Predictive analytics is an area of data research that’s used to make predictions about unknown future events and behaviors. Predictive analytics provides a method of creating focused, targeted prospecting and lead generation. This method is ideal for use with account-based marketing because successfully following leads based on current accounts all comes down to measuring and predicting the behaviors of those accounts. Properly using predictive analytics should look something like this:

  • Create an ideal customer profile based on the data you have
  • Strategically target the right accounts and pick out the best leads within those accounts
  • Craft content and experiences tailored to the preferences and needs of those leads

Where Is The Information Used For Predictive Analytics Sourced From?

The success of predictive analytics relies heavily on the quality of the information you’re able to feed into the process. Predictions are made using the following sources and methods:

  • Data mining
  • Statistics
  • Modeling
  • Machine learning
  • Artificial intelligence

Using The Information At Your Fingertips

Sourcing high-quality information to use for predictive analytics is actually a lot less intimidating than it might first appear. The reality is that today’s customer relationship management (CRM) software and monitoring and administration tool (MAT) software both take in massive amounts of usable information and turn it into digestible formats that can be fed into your process for using predicative analytics. In addition, most modern ad platforms deliver tremendous amounts of information regarding user reactions and behaviors. You can use the information harnessed from these sources to conduct a predictive analysis and improve the following aspects of generating leads:

  • Target selection
  • Message personalization
  • Content quality and effectiveness

Target Account Selection

Target account selection …read more

Read more here:: B2CMarketingInsider

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